/*
 *   This program is free software: you can redistribute it and/or modify
 *   it under the terms of the GNU General Public License as published by
 *   the Free Software Foundation, either version 3 of the License, or
 *   (at your option) any later version.
 *
 *   This program is distributed in the hope that it will be useful,
 *   but WITHOUT ANY WARRANTY; without even the implied warranty of
 *   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *   GNU General Public License for more details.
 *
 *   You should have received a copy of the GNU General Public License
 *   along with this program.  If not, see <http://www.gnu.org/licenses/>.
 */

/*
 *    LWL.java
 *    Copyright (C) 1999-2012 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.classifiers.lazy;

import java.util.Collections;
import java.util.Enumeration;
import java.util.Vector;

import weka.classifiers.Classifier;
import weka.classifiers.SingleClassifierEnhancer;
import weka.classifiers.UpdateableClassifier;
import weka.core.Capabilities;
import weka.core.Capabilities.Capability;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.TechnicalInformation;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformation.Type;
import weka.core.TechnicalInformationHandler;
import weka.core.Utils;
import weka.core.WeightedInstancesHandler;
import weka.core.neighboursearch.LinearNNSearch;
import weka.core.neighboursearch.NearestNeighbourSearch;

/**
 * <!-- globalinfo-start --> Locally weighted learning. Uses an instance-based
 * algorithm to assign instance weights which are then used by a specified
 * WeightedInstancesHandler.<br/>
 * Can do classification (e.g. using naive Bayes) or regression (e.g. using
 * linear regression).<br/>
 * <br/>
 * For more info, see<br/>
 * <br/>
 * Eibe Frank, Mark Hall, Bernhard Pfahringer: Locally Weighted Naive Bayes. In:
 * 19th Conference in Uncertainty in Artificial Intelligence, 249-256,
 * 2003.<br/>
 * <br/>
 * C. Atkeson, A. Moore, S. Schaal (1996). Locally weighted learning. AI
 * Review..
 * <p/>
 * <!-- globalinfo-end -->
 *
 * <!-- technical-bibtex-start --> BibTeX:
 * 
 * <pre>
 * &#64;inproceedings{Frank2003,
 *    author = {Eibe Frank and Mark Hall and Bernhard Pfahringer},
 *    booktitle = {19th Conference in Uncertainty in Artificial Intelligence},
 *    pages = {249-256},
 *    publisher = {Morgan Kaufmann},
 *    title = {Locally Weighted Naive Bayes},
 *    year = {2003}
 * }
 * 
 * &#64;article{Atkeson1996,
 *    author = {C. Atkeson and A. Moore and S. Schaal},
 *    journal = {AI Review},
 *    title = {Locally weighted learning},
 *    year = {1996}
 * }
 * </pre>
 * <p/>
 * <!-- technical-bibtex-end -->
 *
 * <!-- options-start --> Valid options are:
 * <p/>
 * 
 * <pre>
 *  -A
 *  The nearest neighbour search algorithm to use (default: weka.core.neighboursearch.LinearNNSearch).
 * </pre>
 * 
 * <pre>
 *  -K &lt;number of neighbours&gt;
 *  Set the number of neighbours used to set the kernel bandwidth.
 *  (default all)
 * </pre>
 * 
 * <pre>
 *  -U &lt;number of weighting method&gt;
 *  Set the weighting kernel shape to use. 0=Linear, 1=Epanechnikov,
 *  2=Tricube, 3=Inverse, 4=Gaussian.
 *  (default 0 = Linear)
 * </pre>
 * 
 * <pre>
 *  -D
 *  If set, classifier is run in debug mode and
 *  may output additional info to the console
 * </pre>
 * 
 * <pre>
 *  -W
 *  Full name of base classifier.
 *  (default: weka.classifiers.trees.DecisionStump)
 * </pre>
 * 
 * <pre>
 *  
 * Options specific to classifier weka.classifiers.trees.DecisionStump:
 * </pre>
 * 
 * <pre>
 *  -D
 *  If set, classifier is run in debug mode and
 *  may output additional info to the console
 * </pre>
 * 
 * <!-- options-end -->
 *
 * @author Len Trigg (trigg@cs.waikato.ac.nz)
 * @author Eibe Frank (eibe@cs.waikato.ac.nz)
 * @author Ashraf M. Kibriya (amk14[at-the-rate]cs[dot]waikato[dot]ac[dot]nz)
 * @version $Revision$
 */
public class LWL extends SingleClassifierEnhancer implements UpdateableClassifier, WeightedInstancesHandler, TechnicalInformationHandler {

    /** for serialization. */
    static final long serialVersionUID = 1979797405383665815L;

    /** The training instances used for classification. */
    protected Instances m_Train;

    /** The number of neighbours used to select the kernel bandwidth. */
    protected int m_kNN = -1;

    /** The weighting kernel method currently selected. */
    protected int m_WeightKernel = LINEAR;

    /** True if m_kNN should be set to all instances. */
    protected boolean m_UseAllK = true;

    /**
     * The nearest neighbour search algorithm to use. (Default:
     * weka.core.neighboursearch.LinearNNSearch)
     */
    protected NearestNeighbourSearch m_NNSearch = new LinearNNSearch();

    /** The available kernel weighting methods. */
    public static final int LINEAR = 0;
    public static final int EPANECHNIKOV = 1;
    public static final int TRICUBE = 2;
    public static final int INVERSE = 3;
    public static final int GAUSS = 4;
    public static final int CONSTANT = 5;

    /** a ZeroR model in case no model can be built from the data. */
    protected Classifier m_ZeroR;

    /**
     * Returns a string describing classifier.
     * 
     * @return a description suitable for displaying in the explorer/experimenter
     *         gui
     */
    public String globalInfo() {
        return "Locally weighted learning. Uses an instance-based algorithm to " + "assign instance weights which are then used by a specified " + "WeightedInstancesHandler.\n" + "Can do classification (e.g. using naive Bayes) or regression " + "(e.g. using linear regression).\n\n" + "For more info, see\n\n" + getTechnicalInformation().toString();
    }

    /**
     * Returns an instance of a TechnicalInformation object, containing detailed
     * information about the technical background of this class, e.g., paper
     * reference or book this class is based on.
     * 
     * @return the technical information about this class
     */
    public TechnicalInformation getTechnicalInformation() {
        TechnicalInformation result;
        TechnicalInformation additional;

        result = new TechnicalInformation(Type.INPROCEEDINGS);
        result.setValue(Field.AUTHOR, "Eibe Frank and Mark Hall and Bernhard Pfahringer");
        result.setValue(Field.YEAR, "2003");
        result.setValue(Field.TITLE, "Locally Weighted Naive Bayes");
        result.setValue(Field.BOOKTITLE, "19th Conference in Uncertainty in Artificial Intelligence");
        result.setValue(Field.PAGES, "249-256");
        result.setValue(Field.PUBLISHER, "Morgan Kaufmann");

        additional = result.add(Type.ARTICLE);
        additional.setValue(Field.AUTHOR, "C. Atkeson and A. Moore and S. Schaal");
        additional.setValue(Field.YEAR, "1996");
        additional.setValue(Field.TITLE, "Locally weighted learning");
        additional.setValue(Field.JOURNAL, "AI Review");

        return result;
    }

    /**
     * Constructor.
     */
    public LWL() {
        m_Classifier = new weka.classifiers.trees.DecisionStump();
    }

    /**
     * String describing default classifier.
     * 
     * @return the default classifier classname
     */
    protected String defaultClassifierString() {

        return "weka.classifiers.trees.DecisionStump";
    }

    /**
     * Returns an enumeration of the additional measure names produced by the
     * neighbour search algorithm.
     * 
     * @return an enumeration of the measure names
     */
    public Enumeration<String> enumerateMeasures() {
        return m_NNSearch.enumerateMeasures();
    }

    /**
     * Returns the value of the named measure from the neighbour search algorithm.
     * 
     * @param additionalMeasureName the name of the measure to query for its value
     * @return the value of the named measure
     * @throws IllegalArgumentException if the named measure is not supported
     */
    public double getMeasure(String additionalMeasureName) {
        return m_NNSearch.getMeasure(additionalMeasureName);
    }

    /**
     * Returns an enumeration describing the available options.
     *
     * @return an enumeration of all the available options.
     */
    public Enumeration<Option> listOptions() {

        Vector<Option> newVector = new Vector<Option>(3);
        newVector.addElement(new Option("\tThe nearest neighbour search " + "algorithm to use " + "(default: weka.core.neighboursearch.LinearNNSearch).\n", "A", 0, "-A"));
        newVector.addElement(new Option("\tSet the number of neighbours used to set" + " the kernel bandwidth.\n" + "\t(default all)", "K", 1, "-K <number of neighbours>"));
        newVector.addElement(new Option("\tSet the weighting kernel shape to use." + " 0=Linear, 1=Epanechnikov,\n" + "\t2=Tricube, 3=Inverse, 4=Gaussian.\n" + "\t(default 0 = Linear)", "U", 1, "-U <number of weighting method>"));

        newVector.addAll(Collections.list(super.listOptions()));

        return newVector.elements();
    }

    /**
     * Parses a given list of options.
     * <p/>
     *
     * <!-- options-start --> Valid options are:
     * <p/>
     * 
     * <pre>
     *  -A
     *  The nearest neighbour search algorithm to use (default: weka.core.neighboursearch.LinearNNSearch).
     * </pre>
     * 
     * <pre>
     *  -K &lt;number of neighbours&gt;
     *  Set the number of neighbours used to set the kernel bandwidth.
     *  (default all)
     * </pre>
     * 
     * <pre>
     *  -U &lt;number of weighting method&gt;
     *  Set the weighting kernel shape to use. 0=Linear, 1=Epanechnikov,
     *  2=Tricube, 3=Inverse, 4=Gaussian.
     *  (default 0 = Linear)
     * </pre>
     * 
     * <pre>
     *  -D
     *  If set, classifier is run in debug mode and
     *  may output additional info to the console
     * </pre>
     * 
     * <pre>
     *  -W
     *  Full name of base classifier.
     *  (default: weka.classifiers.trees.DecisionStump)
     * </pre>
     * 
     * <pre>
     *  
     * Options specific to classifier weka.classifiers.trees.DecisionStump:
     * </pre>
     * 
     * <pre>
     *  -D
     *  If set, classifier is run in debug mode and
     *  may output additional info to the console
     * </pre>
     * 
     * <!-- options-end -->
     *
     * @param options the list of options as an array of strings
     * @throws Exception if an option is not supported
     */
    public void setOptions(String[] options) throws Exception {

        String knnString = Utils.getOption('K', options);
        if (knnString.length() != 0) {
            setKNN(Integer.parseInt(knnString));
        } else {
            setKNN(-1);
        }

        String weightString = Utils.getOption('U', options);
        if (weightString.length() != 0) {
            setWeightingKernel(Integer.parseInt(weightString));
        } else {
            setWeightingKernel(LINEAR);
        }

        String nnSearchClass = Utils.getOption('A', options);
        if (nnSearchClass.length() != 0) {
            String nnSearchClassSpec[] = Utils.splitOptions(nnSearchClass);
            if (nnSearchClassSpec.length == 0) {
                throw new Exception("Invalid NearestNeighbourSearch algorithm " + "specification string.");
            }
            String className = nnSearchClassSpec[0];
            nnSearchClassSpec[0] = "";

            setNearestNeighbourSearchAlgorithm((NearestNeighbourSearch) Utils.forName(NearestNeighbourSearch.class, className, nnSearchClassSpec));
        } else
            this.setNearestNeighbourSearchAlgorithm(new LinearNNSearch());

        super.setOptions(options);
    }

    /**
     * Gets the current settings of the classifier.
     *
     * @return an array of strings suitable for passing to setOptions
     */
    public String[] getOptions() {

        Vector<String> options = new Vector<String>();

        options.add("-U");
        options.add("" + getWeightingKernel());
        if ((getKNN() == 0) && m_UseAllK) {
            options.add("-K");
            options.add("-1");
        } else {
            options.add("-K");
            options.add("" + getKNN());
        }
        options.add("-A");
        options.add(m_NNSearch.getClass().getName() + " " + Utils.joinOptions(m_NNSearch.getOptions()));
        ;

        Collections.addAll(options, super.getOptions());

        return options.toArray(new String[0]);
    }

    /**
     * Returns the tip text for this property.
     * 
     * @return tip text for this property suitable for displaying in the
     *         explorer/experimenter gui
     */
    public String KNNTipText() {
        return "How many neighbours are used to determine the width of the " + "weighting function (<= 0 means all neighbours).";
    }

    /**
     * Sets the number of neighbours used for kernel bandwidth setting. The
     * bandwidth is taken as the distance to the kth neighbour.
     *
     * @param knn the number of neighbours included inside the kernel bandwidth, or
     *            0 to specify using all neighbors.
     */
    public void setKNN(int knn) {

        m_kNN = knn;
        if (knn <= 0) {
            m_kNN = 0;
            m_UseAllK = true;
        } else {
            m_UseAllK = false;
        }
    }

    /**
     * Gets the number of neighbours used for kernel bandwidth setting. The
     * bandwidth is taken as the distance to the kth neighbour.
     *
     * @return the number of neighbours included inside the kernel bandwidth, or 0
     *         for all neighbours
     */
    public int getKNN() {

        return m_kNN;
    }

    /**
     * Returns the tip text for this property.
     * 
     * @return tip text for this property suitable for displaying in the
     *         explorer/experimenter gui
     */
    public String weightingKernelTipText() {
        return "Determines weighting function. [0 = Linear, 1 = Epnechnikov," + "2 = Tricube, 3 = Inverse, 4 = Gaussian and 5 = Constant. " + "(default 0 = Linear)].";
    }

    /**
     * Sets the kernel weighting method to use. Must be one of LINEAR, EPANECHNIKOV,
     * TRICUBE, INVERSE, GAUSS or CONSTANT, other values are ignored.
     *
     * @param kernel the new kernel method to use. Must be one of LINEAR,
     *               EPANECHNIKOV, TRICUBE, INVERSE, GAUSS or CONSTANT.
     */
    public void setWeightingKernel(int kernel) {

        if ((kernel != LINEAR) && (kernel != EPANECHNIKOV) && (kernel != TRICUBE) && (kernel != INVERSE) && (kernel != GAUSS) && (kernel != CONSTANT)) {
            return;
        }
        m_WeightKernel = kernel;
    }

    /**
     * Gets the kernel weighting method to use.
     *
     * @return the new kernel method to use. Will be one of LINEAR, EPANECHNIKOV,
     *         TRICUBE, INVERSE, GAUSS or CONSTANT.
     */
    public int getWeightingKernel() {

        return m_WeightKernel;
    }

    /**
     * Returns the tip text for this property.
     * 
     * @return tip text for this property suitable for displaying in the
     *         explorer/experimenter gui
     */
    public String nearestNeighbourSearchAlgorithmTipText() {
        return "The nearest neighbour search algorithm to use (Default: LinearNN).";
    }

    /**
     * Returns the current nearestNeighbourSearch algorithm in use.
     * 
     * @return the NearestNeighbourSearch algorithm currently in use.
     */
    public NearestNeighbourSearch getNearestNeighbourSearchAlgorithm() {
        return m_NNSearch;
    }

    /**
     * Sets the nearestNeighbourSearch algorithm to be used for finding nearest
     * neighbour(s).
     * 
     * @param nearestNeighbourSearchAlgorithm - The NearestNeighbourSearch class.
     */
    public void setNearestNeighbourSearchAlgorithm(NearestNeighbourSearch nearestNeighbourSearchAlgorithm) {
        m_NNSearch = nearestNeighbourSearchAlgorithm;
    }

    /**
     * Returns default capabilities of the classifier.
     *
     * @return the capabilities of this classifier
     */
    public Capabilities getCapabilities() {
        Capabilities result;

        if (m_Classifier != null) {
            result = m_Classifier.getCapabilities();
        } else {
            result = super.getCapabilities();
        }

        result.setMinimumNumberInstances(0);

        // set dependencies
        for (Capability cap : Capability.values())
            result.enableDependency(cap);

        return result;
    }

    /**
     * Generates the classifier.
     *
     * @param instances set of instances serving as training data
     * @throws Exception if the classifier has not been generated successfully
     */
    public void buildClassifier(Instances instances) throws Exception {

        if (!(m_Classifier instanceof WeightedInstancesHandler)) {
            throw new IllegalArgumentException("Classifier must be a " + "WeightedInstancesHandler!");
        }

        // can classifier handle the data?
        getCapabilities().testWithFail(instances);

        // remove instances with missing class
        instances = new Instances(instances);
        instances.deleteWithMissingClass();

        // only class? -> build ZeroR model
        if (instances.numAttributes() == 1) {
            System.err.println("Cannot build model (only class attribute present in data!), " + "using ZeroR model instead!");
            m_ZeroR = new weka.classifiers.rules.ZeroR();
            m_ZeroR.buildClassifier(instances);
            return;
        } else {
            m_ZeroR = null;
        }

        m_Train = new Instances(instances, 0, instances.numInstances());

        m_NNSearch.setInstances(m_Train);
    }

    /**
     * Adds the supplied instance to the training set.
     *
     * @param instance the instance to add
     * @throws Exception if instance could not be incorporated successfully
     */
    public void updateClassifier(Instance instance) throws Exception {

        if (m_Train == null) {
            throw new Exception("No training instance structure set!");
        } else if (m_Train.equalHeaders(instance.dataset()) == false) {
            throw new Exception("Incompatible instance types\n" + m_Train.equalHeadersMsg(instance.dataset()));
        }
        if (!instance.classIsMissing()) {
            m_NNSearch.update(instance);
            m_Train.add(instance);
        }
    }

    /**
     * Calculates the class membership probabilities for the given test instance.
     *
     * @param instance the instance to be classified
     * @return preedicted class probability distribution
     * @throws Exception if distribution can't be computed successfully
     */
    public double[] distributionForInstance(Instance instance) throws Exception {

        // default model?
        if (m_ZeroR != null) {
            return m_ZeroR.distributionForInstance(instance);
        }

        if (m_Train.numInstances() == 0) {
            throw new Exception("No training instances!");
        }

        m_NNSearch.addInstanceInfo(instance);

        int k = m_Train.numInstances();
        if ((!m_UseAllK && (m_kNN < k)) /*
                                         * && !(m_WeightKernel==INVERSE || m_WeightKernel==GAUSS)
                                         */ ) {
            k = m_kNN;
        }

        Instances neighbours = m_NNSearch.kNearestNeighbours(instance, k);
        double distances[] = m_NNSearch.getDistances();

        if (m_Debug) {
            System.out.println("Test Instance: " + instance);
            System.out.println("For " + k + " kept " + neighbours.numInstances() + " out of " + m_Train.numInstances() + " instances.");
        }

        // IF LinearNN has skipped so much that <k neighbours are remaining.
        if (k > distances.length)
            k = distances.length;

        if (m_Debug) {
            System.out.println("Instance Distances");
            for (int i = 0; i < distances.length; i++) {
                System.out.println("" + distances[i]);
            }
        }

        // Determine the bandwidth
        double bandwidth = distances[k - 1];

        // Check for bandwidth zero
        if (bandwidth <= 0) {
            // if the kth distance is zero than give all instances the same weight
            for (int i = 0; i < distances.length; i++)
                distances[i] = 1;
        } else {
            // Rescale the distances by the bandwidth
            for (int i = 0; i < distances.length; i++)
                distances[i] = distances[i] / bandwidth;
        }

        // Pass the distances through a weighting kernel
        for (int i = 0; i < distances.length; i++) {
            switch (m_WeightKernel) {
            case LINEAR:
                distances[i] = 1.0001 - distances[i];
                break;
            case EPANECHNIKOV:
                distances[i] = 3 / 4D * (1.0001 - distances[i] * distances[i]);
                break;
            case TRICUBE:
                distances[i] = Math.pow((1.0001 - Math.pow(distances[i], 3)), 3);
                break;
            case CONSTANT:
                // System.err.println("using constant kernel");
                distances[i] = 1;
                break;
            case INVERSE:
                distances[i] = 1.0 / (1.0 + distances[i]);
                break;
            case GAUSS:
                distances[i] = Math.exp(-distances[i] * distances[i]);
                break;
            }
        }

        if (m_Debug) {
            System.out.println("Instance Weights");
            for (int i = 0; i < distances.length; i++) {
                System.out.println("" + distances[i]);
            }
        }

        // Set the weights on the training data
        double sumOfWeights = 0, newSumOfWeights = 0;
        for (int i = 0; i < distances.length; i++) {
            double weight = distances[i];
            Instance inst = (Instance) neighbours.instance(i);
            sumOfWeights += inst.weight();
            newSumOfWeights += inst.weight() * weight;
            inst.setWeight(inst.weight() * weight);
            // weightedTrain.add(newInst);
        }

        // Rescale weights
        for (int i = 0; i < neighbours.numInstances(); i++) {
            Instance inst = neighbours.instance(i);
            inst.setWeight(inst.weight() * sumOfWeights / newSumOfWeights);
        }

        // Create a weighted classifier
        m_Classifier.buildClassifier(neighbours);

        if (m_Debug) {
            System.out.println("Classifying test instance: " + instance);
            System.out.println("Built base classifier:\n" + m_Classifier.toString());
        }

        // Return the classifier's predictions
        return m_Classifier.distributionForInstance(instance);
    }

    /**
     * Returns a description of this classifier.
     *
     * @return a description of this classifier as a string.
     */
    public String toString() {

        // only ZeroR model?
        if (m_ZeroR != null) {
            StringBuffer buf = new StringBuffer();
            buf.append(this.getClass().getName().replaceAll(".*\\.", "") + "\n");
            buf.append(this.getClass().getName().replaceAll(".*\\.", "").replaceAll(".", "=") + "\n\n");
            buf.append("Warning: No model could be built, hence ZeroR model is used:\n\n");
            buf.append(m_ZeroR.toString());
            return buf.toString();
        }

        if (m_Train == null) {
            return "Locally weighted learning: No model built yet.";
        }
        String result = "Locally weighted learning\n" + "===========================\n";

        result += "Using classifier: " + m_Classifier.getClass().getName() + "\n";

        switch (m_WeightKernel) {
        case LINEAR:
            result += "Using linear weighting kernels\n";
            break;
        case EPANECHNIKOV:
            result += "Using epanechnikov weighting kernels\n";
            break;
        case TRICUBE:
            result += "Using tricube weighting kernels\n";
            break;
        case INVERSE:
            result += "Using inverse-distance weighting kernels\n";
            break;
        case GAUSS:
            result += "Using gaussian weighting kernels\n";
            break;
        case CONSTANT:
            result += "Using constant weighting kernels\n";
            break;
        }
        result += "Using " + (m_UseAllK ? "all" : "" + m_kNN) + " neighbours";
        return result;
    }

    /**
     * Main method for testing this class.
     *
     * @param argv the options
     */
    public static void main(String[] argv) {
        runClassifier(new LWL(), argv);
    }
}
